Intelligent Cloud-SAP Software Framework for AI-Driven Healthcare Analytics and Process Optimization
DOI:
https://doi.org/10.15662/IJRAI.2025.0806805Keywords:
Artificial Intelligence, Cloud Computing, SAP Integration, Healthcare Analytics, Predictive Diagnostics, Electronic Health Records, Machine Learning, Digital TransformationAbstract
This research presents an AI-enabled Cloud-SAP Software Framework designed to revolutionize healthcare data management, analytics, and decision-making. The framework integrates Artificial Intelligence (AI) with cloud-based SAP platforms to improve system interoperability, allowing real-time patient data analysis, predictive diagnostics, and automation of key administrative operations. Utilizing advanced machine learning models and secure cloud infrastructure, it delivers precise insights into patient outcomes, resource utilization, and workflow efficiency. Furthermore, the framework enhances data-driven clinical decision-making through seamless integration with Electronic Health Records (EHRs) and IoT-connected medical devices. Overall, this intelligent Cloud-SAP architecture provides a scalable, secure, and compliant ecosystem that accelerates digital transformation and optimizes healthcare service delivery.
References
1. Al-Mashari, M. (2003). A process change-oriented model for ERP application. International Journal of Human–Computer Interaction, 16(1), 39–55.
2. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
3. Nurtaz Begum, A., Samira Alam, C., & KM, Z. (2025). Enhancing Data Privacy in National Business Infrastructure: Measures that Concern the Analytics and Finance Industry. American Journal of Technology Advancement, 2(10), 46-54.
4. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
5. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. https://www.researchgate.net/profile/Binu-C-T/publication/383037713_Enhancing_Cloud_Security_through_Machine_Learning-Based_Threat_Prevention_and_Monitoring_The_Development_and_Evaluation_of_the_PBPM_Framework/links/66b99cfb299c327096c1774a/Enhancing-Cloud-Security-through-Machine-Learning-Based-Threat-Prevention-and-Monitoring-The-Development-and-Evaluation-of-the-PBPM-Framework.pdf
6. Benlian, A., Hess, T., & Buxmann, P. (2009). Drivers of SaaS adoption: An empirical study. Information Systems Journal, 19(5), 525–548.
7. Davenport, T. H. (2000). Mission Critical: Realizing the Promise of Enterprise Systems. Harvard Business School Press.
8. Gosangi, S. R. (2023). Reimagining Government Financial Systems: A Scalable ERP Upgrade Strategy for Modern Public Sector Needs. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8001-8005.
9. Kumar, R., S. Kumar, and P. Bansal. "Disease detection in apple leaves using deep convolutional neural network." (2021)
10. Joyce, S., Pasumarthi, A., & Anbalagan, B. SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURE-NATIVE TOOLS AND PRACTICES.
11. Fahmideh, M., Daneshgar, F., Beydoun, G., & Rabhi, F. (2020). Challenges in migrating legacy software systems to the cloud: An empirical study. arXiv Preprint.
12. Shashank, P. S. R. B., Anand, L., & Pitchai, R. (2024, December). MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation. In 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) (pp. 157-161). IEEE.
13. SIVARAJU, P. S. ZERO-TRUST SECURITY AND MFA DEPLOYMENT AT SCALE: ELIMINATING VULNERABILITIES IN GLOBAL FULFILLMENT NETWORKS., researchgate.net/profile/Phani-Santhosh-Sivaraju/publication/395722579_ZERO-TRUST_SECURITY_AND_MFA_DEPLOYMENT_AT_SCALE_ELIMINATING_VULNERABILITIES_IN_GLOBAL_FULFILLMENT_NETWORKS/links/68d1e8cb11d348252ba6db60/ZERO-TRUST-SECURITY-AND-MFA-DEPLOYMENT-AT-SCALE-ELIMINATING-VULNERABILITIES-IN-GLOBAL-FULFILLMENT-NETWORKS.pdf
14. Jaiswal, C. (2022). AI and cloud-driven approaches for modernising traditional ERP systems. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 218–225.
15. Perumalsamy, J., & Christadoss, J. (2024). Predictive Modeling for Autonomous Detection and Correction of AI-Agent Hallucinations Using Transformer Networks. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 581-603.
16. Balaji, P. C., & Sugumar, R. (2025, April). Accurate thresholding of grayscale images using Mayfly algorithm comparison with Cuckoo search algorithm. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020114). AIP Publishing LLC.
17. Manda, P. (2023). LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8714-8725.
18. Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: A systematic review. IEEE Software, 35(2), 16-25.
19. Lin, T. (2024). The role of generative AI in proactive incident management: Transforming infrastructure operations. International Journal of Innovative Research in Science, Engineering and Technology, 13(12), Article — . https://doi.org/10.15680/IJIRSET.2024.1312014
20. Adari, Vijay Kumar, “Interoperability and Data Modernization: Building a Connected Banking Ecosystem,” International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 6, pp.653-662, Nov-Dec 2024. DOI:https://doi.org/10.5281/zenodo.14219429.
21. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
22. Sugumar, R. (2025, March). Diabetes Insights: Gene Expression Profiling with Machine Learning and NCBI Datasets. In 2025 7th International Conference on Intelligent Sustainable Systems (ICISS) (pp. 712-718). IEEE.
23. Konda, S. K. (2025). Designing scalable integrated building management systems for large-scale venues: A systems architecture perspective. International Journal of Computer Engineering and Technology, 16(3), 299–314. https://doi.org/10.34218/IJCET_16_03_022
24. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
25. A.M., Arul Raj, A. M., R., Sugumar, Rajendran, Annie Grace Vimala, G. S., Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection, Bulletin of Electrical Engineering and Informatics, Volume 13, Issue 3, 2024, pp.1935-1942, https://doi.org/10.11591/eei.v13i3.6393.





